Influence of muscle fibre shortening on estimates of conduction velocity and spectral frequencies from surface electromyographic signals

  • E. Schulte
  • D. Farina
  • R. Merletti
  • G. Rau
  • C. Disselhorst-Klug


The study of surface electromyographic (EMG) signals under dynamic contractions is becoming increasingly important. However, knowledge of the methodological issues that may affect such analysis is still limited. The aim of the study was to analyse the effect of fibre shortening on estimates of conduction velocity (CV) and mean power spectral frequency (MNF) from surface EMG signals. Single fibre action potentials were simulated, as detected by commonly used spatial filters, for different fibre lengths. No physiological modifications were included with changes in fibre length, and thus only geometrical artifacts related to fibre shortening were investigated. The simulation results showed that the dependence of CV and MNF on fibre shortening is affected by the fibre location, electrode position and the spatial filter applied. With shortening of up to 50% for a fibre of 50 mm semi-length, the variations in CV and MNF estimates with shortening in bipolar recordings were 0.5% (CV) and 0.7% (MNF) for superficial fibres, and 3.6% and 5.1% for deeper fibres. Using the longitudinal double differential filter, under the same conditions, the percent variation was 0% and 0.2%, and 24.7% and 15.8%, respectively. The main conclusions were, first, muscle fibre shortening can significantly affect estimates of CV and MNF, especially for short fibre lengths. However, for long (semi-length>50mm) and superficial fibres, this effect is limited for shortenings of up to 50% of the initial fibre length. Secondly, CV and MNF are almost equally affected by changes in muscle length; and, thirdly, sensitivity to fibre shortening depends on the spatial filter applied for signal detection.


Surface EMG Dynamic EMG Spatial filtering EMG modelling 


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Copyright information

© IFMBE 2004

Authors and Affiliations

  • E. Schulte
    • 1
  • D. Farina
    • 2
  • R. Merletti
    • 2
  • G. Rau
    • 1
  • C. Disselhorst-Klug
    • 1
  1. 1.Institute for Biomedical TechnologiesHelmholtz InstituteAachenGermany
  2. 2.Dipartimento di Elettronica, Laboratorio di Ingegneria del Sistema Neuromuscolare (LISiN)Politecnico di TorinoTorinoItaly

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